Ensemble-based method of fraud detection at self-checkouts in retail
Date
2019-06-26
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Abstract
The authors consider the problem of fraud
detection at self-checkouts in retail in condition of unbalanced
data set. A new ensemble-based method is proposed for its
effective solution. The developed method involves two main
steps: application of the preprocessing procedures and the
Random Forest algorithm. The step-by-step implementation of
the preprocessing stage involves the sequential execution of
such procedures over the input data: scaling by maximal element
in a column with row-wise scaling by Euclidean norm,
weighting by correlation and applying polynomial extension.
For polynomial extension Ito decomposition of the second
degree is used. The simulation of the method was carried out on
real data. Evaluating performance was based on the use of cost
matrix. The experimental comparison of the effectiveness of the
developed ensemble-based method with a number of existing
(simples and ensembles) demonstrates the best performance of
the developed method. Experimental studies of changing the
parameters of the Random Forest both for the basic algorithm
and for the developed method demonstrate a significant
improvement of the investigated efficiency measures of the
latter. It is the result of all steps of the preprocessing stage of the
developed method use.
Description
Keywords
classification, Ensemble-based method, Random Forest, fraud detection, retail, Ito decomposition, imbalanced dataset
Citation
Vitynskyi P. Ensemble-based method of fraud detection at self-checkouts in retail / P. Vitynskyi, R. Tkachenko, I. Izonin // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 3–8.